{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from darts import SeasonalityMode\n",
    "from darts.models import NaiveSeasonal, NaiveDrift\n",
    "from darts.models import Prophet, ExponentialSmoothing, AutoARIMA, Theta, FourTheta\n",
    "from darts.models import StandardRegressionModel\n",
    "from darts.utils.statistics import check_seasonality, remove_seasonality, extract_trend_and_seasonality\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from M4_metrics import owa_m4, smape_m4, mase_m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from darts.utils.timeseries_generation import constant_timeseries as ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "download = False\n",
    "preprocess = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download and create TimeSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if download:\n",
    "    %run -i \"download_data_M4.py\"\n",
    "if preprocess:\n",
    "    %run -i \"create_ts.py\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_categories = ['Macro', 'Micro', 'Demographic', 'Industry', 'Finance', 'Other']\n",
    "data_freq = ['Yearly', 'Quarterly', 'Monthly', 'Weekly', 'Daily', 'Hourly']\n",
    "info_dataset = pd.read_csv('dataset/M4-info.csv', delimiter=',').set_index('M4id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Horizon</th>\n",
       "      <th>SP</th>\n",
       "      <th>StartingDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Y1</th>\n",
       "      <td>Macro</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yearly</td>\n",
       "      <td>01-01-79 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y2</th>\n",
       "      <td>Macro</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yearly</td>\n",
       "      <td>01-01-79 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y3</th>\n",
       "      <td>Macro</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yearly</td>\n",
       "      <td>01-01-79 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y4</th>\n",
       "      <td>Macro</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yearly</td>\n",
       "      <td>01-01-79 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y5</th>\n",
       "      <td>Macro</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yearly</td>\n",
       "      <td>01-01-79 12:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     category  Frequency  Horizon      SP    StartingDate\n",
       "M4id                                                     \n",
       "Y1      Macro          1        6  Yearly  01-01-79 12:00\n",
       "Y2      Macro          1        6  Yearly  01-01-79 12:00\n",
       "Y3      Macro          1        6  Yearly  01-01-79 12:00\n",
       "Y4      Macro          1        6  Yearly  01-01-79 12:00\n",
       "Y5      Macro          1        6  Yearly  01-01-79 12:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other', 'Macro', 'Industry', 'Finance', 'Demographic', 'Micro'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_dataset.filter(regex='W', axis=0).category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Horizon</th>\n",
       "      <th>StartingDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Daily</th>\n",
       "      <td>4227</td>\n",
       "      <td>4227</td>\n",
       "      <td>4227</td>\n",
       "      <td>4227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hourly</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly</th>\n",
       "      <td>48000</td>\n",
       "      <td>48000</td>\n",
       "      <td>48000</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarterly</th>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekly</th>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           category  Frequency  Horizon  StartingDate\n",
       "SP                                                   \n",
       "Daily          4227       4227     4227          4227\n",
       "Hourly          414        414      414           414\n",
       "Monthly       48000      48000    48000         48000\n",
       "Quarterly     24000      24000    24000         24000\n",
       "Weekly          359        359      359           359\n",
       "Yearly        23000      23000    23000         23000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_dataset.groupby('SP').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SP\n",
      "Daily         [1]\n",
      "Hourly       [24]\n",
      "Monthly      [12]\n",
      "Quarterly     [4]\n",
      "Weekly        [1]\n",
      "Yearly        [1]\n",
      "Name: Frequency, dtype: object\n",
      "SP\n",
      "Daily        [14]\n",
      "Hourly       [48]\n",
      "Monthly      [18]\n",
      "Quarterly     [8]\n",
      "Weekly       [13]\n",
      "Yearly        [6]\n",
      "Name: Horizon, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(info_dataset.groupby('SP').Frequency.unique())\n",
    "print(info_dataset.groupby('SP').Horizon.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluating methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = 'Hourly'\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7fda80d505449e8a274f87600712dfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mase_all = []\n",
    "smape_all = []\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "for train, test in tqdm(zip(ts_train, ts_test)):\n",
    "    # remove seasonality\n",
    "    train_des=train\n",
    "    seasonOut = ct(length=len(test), freq=train.freq_str(), start_ts=test.start_time())\n",
    "    season = ct(length=len(train), freq=train.freq_str(), start_ts=train.start_time())\n",
    "    if m > 1:\n",
    "        if check_seasonality(train, m=m, max_lag=2*m):\n",
    "            _, season = extract_trend_and_seasonality(train, m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "            train_des = remove_seasonality(train, freq=m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "            seasonOut = season[-m:].shift(m)\n",
    "            seasonOut = seasonOut.append_values(seasonOut.values())[:len(test)]\n",
    "    # model selection\n",
    "    naiveSeason = NaiveSeasonal(K=m)\n",
    "    naive2 = NaiveSeasonal(K=1)\n",
    "    ses = ExponentialSmoothing(trend=None, seasonal=None)\n",
    "    holt = ExponentialSmoothing(seasonal=None, damped=False, trend='additive')\n",
    "    damp = ExponentialSmoothing(seasonal=None, damped=True, trend='additive')\n",
    "    fourtheta = FourTheta.select_best_model(train, thetas=[1,2,3], m=m)\n",
    "    # model fitting\n",
    "    naiveSeason.fit(train)\n",
    "    naive2.fit(train_des)\n",
    "    fourtheta.fit(train)\n",
    "    ses.fit(train_des)\n",
    "    holt.fit(train_des)\n",
    "    damp.fit(train_des)\n",
    "    # forecasting\n",
    "    forecast_naiveSeason = naiveSeason.predict(len(test))\n",
    "    forecast_naive2 = naive2.predict(len(test)) * seasonOut\n",
    "    forecast_fourtheta = fourtheta.predict(len(test))\n",
    "    forecast_ses = ses.predict(len(test))*seasonOut\n",
    "    forecast_holt = holt.predict(len(test))*seasonOut\n",
    "    forecast_damp = damp.predict(len(test))*seasonOut\n",
    "    # baseline constant weight ensembling\n",
    "    forecast_comb = ((forecast_ses + forecast_holt + forecast_damp) / 3)\n",
    "    \n",
    "    mase_all.append(np.vstack([\n",
    "                               mase_m4(train, test, forecast_naiveSeason, m=m),\n",
    "                               mase_m4(train, test, forecast_naive2, m=m),\n",
    "                               mase_m4(train, test, forecast_fourtheta, m=m),\n",
    "                               mase_m4(train, test, forecast_ses, m=m),\n",
    "                               mase_m4(train, test, forecast_holt, m=m),\n",
    "                               mase_m4(train, test, forecast_damp, m=m),\n",
    "                               mase_m4(train, test, forecast_comb, m=m),\n",
    "                              ]))\n",
    "    smape_all.append(np.vstack([\n",
    "                                smape_m4(test, forecast_naiveSeason),\n",
    "                                smape_m4(test, forecast_naive2),\n",
    "                                smape_m4(test, forecast_fourtheta),\n",
    "                                smape_m4(test, forecast_ses),\n",
    "                                smape_m4(test, forecast_holt),\n",
    "                                smape_m4(test, forecast_damp),\n",
    "                                smape_m4(test, forecast_comb),\n",
    "                               ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MASE; naiveSeason: 1.193, naive2: 2.400, 4Theta: 1.768,\n",
      "SES: 2.373, Holt: 8.728, Damp: 2.938, Comb: 4.237\n",
      "\n",
      "sMAPE; naiveSeason: 13.912, naive2: 18.820, 4Theta: 17.510,\n",
      "SES: 18.216, Holt: 27.997, Damp: 19.194, Comb: 21.475\n",
      "\n",
      "OWA; naiveSeason: 0.628, naive2: 1.013, 4Theta: 0.845,\n",
      "SES: 0.991, Holt: 2.584, Damp: 1.135, Comb: 1.469\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"MASE; naiveSeason: {:.3f}, naive2: {:.3f}, 4Theta: {:.3f},\\n\"\n",
    "       \"SES: {:.3f}, Holt: {:.3f}, Damp: {:.3f}, Comb: {:.3f}\\n\".format(*tuple(np.stack(mase_all).mean(axis=(0,2)))))\n",
    "print(\"sMAPE; naiveSeason: {:.3f}, naive2: {:.3f}, 4Theta: {:.3f},\\n\"\n",
    "       \"SES: {:.3f}, Holt: {:.3f}, Damp: {:.3f}, Comb: {:.3f}\\n\".format(*tuple(np.stack(smape_all).mean(axis=(0,2)))))\n",
    "print(\"OWA; naiveSeason: {:.3f}, naive2: {:.3f}, 4Theta: {:.3f},\\n\"\n",
    "       \"SES: {:.3f}, Holt: {:.3f}, Damp: {:.3f}, Comb: {:.3f}\\n\".format(*tuple(owa_m4(freq, \n",
    "                                                                        np.stack(smape_all).mean(axis=(0,2)), \n",
    "                                                                        np.stack(mase_all).mean(axis=(0,2))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17a812b07c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[-2*m:].plot(label='train')\n",
    "test.plot(label='test')\n",
    "forecast_naiveSeason.plot(label='naive seasonal')\n",
    "forecast_naive2.plot(label='naive2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.nanmean(np.stack(mase_all), axis=(2,))[:,3], bins=100, label='4Theta')\n",
    "plt.hist(np.nanmean(np.stack(mase_all), axis=(2,))[:,0], bins=30, label='naiveSeason', alpha=0.7)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = 'Monthly'\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "train_des=train\n",
    "seasonOut = ct(length=len(test), freq=train.freq_str(), start_ts=test.start_time())\n",
    "season = ct(length=len(train), freq=train.freq_str(), start_ts=train.start_time())\n",
    "if m > 1:\n",
    "    if check_seasonality(train, m=int(m), max_lag=2*m):\n",
    "        _, season = extract_trend_and_seasonality(train, m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "        train_des = remove_seasonality(train, freq=m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "        seasonOut = season[-m:].shift(m)\n",
    "        seasonOut = seasonOut.append_values(seasonOut.values())[:len(test)]\n",
    "    else:\n",
    "        m = 1\n",
    "# model selection\n",
    "naiveSeason = NaiveSeasonal(K=m)\n",
    "naive2 = NaiveSeasonal(K=1)\n",
    "prophet = Prophet(yearly_seasonality=True, changepoint_range=0.95)\n",
    "arima = AutoARIMA()\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[1, 2, 3], m=m)\n",
    "# model fitting\n",
    "naiveSeason.fit(train)\n",
    "naive2.fit(train_des)\n",
    "fourtheta.fit(train)\n",
    "prophet.fit(train)\n",
    "arima.fit(train)\n",
    "# forecasting\n",
    "forecast_naiveSeason = naiveSeason.predict(len(test))\n",
    "forecast_naive2 = naive2.predict(len(test)) * seasonOut\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_arima = arima.predict(len(test))\n",
    "forecast_prophet = prophet.predict(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17a82c29cc8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[-m:].plot(label='train')\n",
    "test.plot(label='test', lw=3)\n",
    "forecast_naiveSeason.plot(label='naiveS')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_naive2.plot(label='naive2')\n",
    "forecast_arima.plot(label='ARIMA')\n",
    "forecast_prophet.pd_series().plot(label='prophet')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4Theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.LINEAR, model:ModelMode.MULTIPLICATIVE, seasonality:SeasonalityMode.MULTIPLICATIVE)\n",
      "Theta MASE: 1.594\n",
      "4Theta MASE: 1.485\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Yearly'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.LINEAR, model:ModelMode.MULTIPLICATIVE, seasonality:SeasonalityMode.MULTIPLICATIVE)\n",
      "Theta MASE: 1.373\n",
      "4Theta MASE: 1.501\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Quarterly'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.EXPONENTIAL, model:ModelMode.ADDITIVE, seasonality:SeasonalityMode.ADDITIVE)\n",
      "Theta MASE: 1.053\n",
      "4Theta MASE: 0.992\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Monthly'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.EXPONENTIAL, model:ModelMode.MULTIPLICATIVE, seasonality:SeasonalityMode.MULTIPLICATIVE)\n",
      "Theta MASE: 10.147\n",
      "4Theta MASE: 9.599\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Weekly'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.LINEAR, model:ModelMode.ADDITIVE, seasonality:SeasonalityMode.MULTIPLICATIVE)\n",
      "Theta MASE: 2.055\n",
      "4Theta MASE: 2.049\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Daily'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 1\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:2, curve:TrendMode.EXPONENTIAL, model:ModelMode.ADDITIVE, seasonality:SeasonalityMode.MULTIPLICATIVE)\n",
      "Theta MASE: 0.649\n",
      "4Theta MASE: 0.646\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Hourly'\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 0\n",
    "train = ts_train[_id]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=[2], m=m)\n",
    "theta = Theta(seasonality_period=m)\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's try to find a better theta, and a better frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4Theta(theta:10.0, curve:TrendMode.EXPONENTIAL, model:ModelMode.MULTIPLICATIVE, seasonality:SeasonalityMode.ADDITIVE)\n",
      "Theta MASE: 1.649\n",
      "4Theta MASE: 1.074\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 'Weekly'\n",
    "plt.figure()\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "_id = 2\n",
    "train = ts_train[_id][-100:]\n",
    "test = ts_test[_id]\n",
    "fourtheta = FourTheta.select_best_model(train, thetas=np.linspace(-1, 10, 90), m=None)\n",
    "theta = Theta(theta=(2 - fourtheta.theta))\n",
    "print(fourtheta)\n",
    "\n",
    "fourtheta.fit(train)\n",
    "theta.fit(train)\n",
    "\n",
    "forecast_fourtheta = fourtheta.predict(len(test))\n",
    "forecast_theta = theta.predict(len(test))\n",
    "\n",
    "test.plot(label='test')\n",
    "forecast_fourtheta.plot(label='4theta')\n",
    "forecast_theta.plot(label='classic theta')\n",
    "plt.title(freq + ' frequency')\n",
    "print(\"Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_theta))))\n",
    "print(\"4Theta MASE: {:.3f}\".format(np.mean(mase_m4(train, test, forecast_fourtheta))))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### run evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_baseline = False\n",
    "if run_baseline:\n",
    "    %run -i \"evaluate_baselines.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_thetas = False\n",
    "if run_thetas:\n",
    "    %run -i \"evaluate_theta_methods.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_fft = False\n",
    "if run_fft:\n",
    "    %run -i \"evaluate_fft.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_arima = False\n",
    "if run_arima:\n",
    "    %run -i \"evaluate_arima.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_prophet = False\n",
    "if run_prophet:\n",
    "    %run -i \"evaluate_prophet.py\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evaluate_ensembling import naive2_groe, groe_owa, DeseasonForecastingModel\n",
    "deseason_model = DeseasonForecastingModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ceff6262b2a4050926d35051bd9ebde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "freq = 'Monthly'\n",
    "ts_train = pickle.load(open(\"dataset/train_\"+freq+\".pkl\", \"rb\"))\n",
    "ts_test = pickle.load(open(\"dataset/test_\"+freq+\".pkl\", \"rb\"))\n",
    "\n",
    "mase_all = []\n",
    "smape_all = []\n",
    "m = int(info_dataset.Frequency[freq[0]+'1'])\n",
    "for train, test in tqdm(zip(ts_train[:5], ts_test[:5])):\n",
    "    # remove seasonality\n",
    "    train_des=train\n",
    "    seasonOut = 1\n",
    "    season = ct(length=len(train), freq=train.freq_str(), start_ts=train.start_time())\n",
    "    if m > 1:\n",
    "        if check_seasonality(train, m=int(m), max_lag=2*m):\n",
    "            pass\n",
    "            _, season = extract_trend_and_seasonality(train, m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "            train_des = remove_seasonality(train, freq=m, model=SeasonalityMode.MULTIPLICATIVE)\n",
    "            seasonOut = season[-m:].shift(m)\n",
    "            seasonOut = seasonOut.append_values(seasonOut.values())[:len(test)]\n",
    "    # model choice\n",
    "    naiveSeason = NaiveSeasonal(K=m)\n",
    "    naiveDrift = NaiveDrift()\n",
    "    naive2 = NaiveSeasonal(K=1)\n",
    "    ses = ExponentialSmoothing(trend=None, seasonal=None, seasonal_periods=m)\n",
    "    holt = ExponentialSmoothing(seasonal=None, damped=False, trend='additive', seasonal_periods=m)\n",
    "    damp = ExponentialSmoothing(seasonal=None, damped=True, trend='additive', seasonal_periods=m)\n",
    "    # prophet = Prophet(yearly_seasonality=True, weekly_seasonality=False, daily_seasonality=False)\n",
    "    \n",
    "    fourtheta = FourTheta.select_best_model(train, thetas=[1, 2, 3], m=m)\n",
    "    theta = Theta(theta=2, season_mode=SeasonalityMode.MULTIPLICATIVE, seasonality_period=m)\n",
    "    models_simple = [naiveSeason, theta, fourtheta]\n",
    "    models_des = [naive2, ses, holt, damp]\n",
    "\n",
    "    # linear regression (with constraints)\n",
    "    def train_pred(id_start=None, id_end=None):\n",
    "        for m in models_simple:\n",
    "            m.fit(train[id_start:id_end])\n",
    "        for m in models_des:\n",
    "            m.fit(train_des[id_start:id_end])\n",
    "        models_simple_predictions = [m.predict(len(test))\n",
    "                                     for m in models_simple]\n",
    "        id_fin = id_end+len(test)\n",
    "        if id_fin == 0:\n",
    "            id_fin = None\n",
    "        models_des_predictions = [m.predict(len(test)) * (seasonOut if id_end is None else season[id_end:id_fin])\n",
    "                                  for m in models_des]\n",
    "\n",
    "        model_predictions = models_simple_predictions + models_des_predictions\n",
    "        \n",
    "        return model_predictions\n",
    "    \n",
    "    val_predictions = train_pred(id_end=-len(test))\n",
    "\n",
    "    regr_model = StandardRegressionModel(train_n_points=len(test), \n",
    "                                         model=LassoCV(positive=True, fit_intercept=False, max_iter=5000))\n",
    "    target_val = train.slice_intersect(val_predictions[0])\n",
    "    regr_model.fit(val_predictions, target_val)\n",
    "    bktest_pred = val_predictions\n",
    "    \n",
    "    for mod in models_simple:\n",
    "        mod.fit(train)\n",
    "    for mod in models_des:\n",
    "        mod.fit(train_des)\n",
    "    \n",
    "    models_simple_predictions = [mod.predict(len(test))\n",
    "                                 for mod in models_simple]\n",
    "    models_des_predictions = [mod.predict(len(test)) * seasonOut\n",
    "                              for mod in models_des]\n",
    "\n",
    "    model_predictions = models_simple_predictions + models_des_predictions\n",
    "    \n",
    "    regr_model.model.coef_ = regr_model.model.coef_/np.sum(regr_model.model.coef_)\n",
    "    \n",
    "    ensemble_pred = regr_model.predict(model_predictions)\n",
    "    \n",
    "    # Mean ensembling\n",
    "    mean_pred = 0\n",
    "    for pred in model_predictions:\n",
    "        mean_pred = pred + mean_pred\n",
    "    mean_pred = mean_pred/len(model_predictions)\n",
    "    \n",
    "    ## GROE OWA\n",
    "    criterion = []\n",
    "    criterion.append(groe_owa(train, naiveSeason, max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, theta, max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, fourtheta, max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, deseason_model(NaiveSeasonal(K=1), m), max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, deseason_model(ses, m), max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, deseason_model(holt, m), max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    criterion.append(groe_owa(train, deseason_model(damp, m), max(5, len(train)-len(test)), int(np.floor(len(test)/6)), 6, m))\n",
    "    \n",
    "    Score = 1/np.array(criterion)\n",
    "    pesos = Score/Score.sum()\n",
    "    \n",
    "    groe_ensemble = 0\n",
    "    for prediction, weight in zip(model_predictions, pesos):\n",
    "        groe_ensemble = prediction * weight + groe_ensemble\n",
    "    \n",
    "    # BO3 ensembling\n",
    "    score = np.argsort(Score)[::-1][:3]\n",
    "    pesos2 = Score[score]/Score[score].sum()\n",
    "    \n",
    "    bo3_ensemble = 0\n",
    "    bo3_mean = 0\n",
    "    for i, model in enumerate(score):\n",
    "            bo3_ensemble = model_predictions[model]*pesos2[i] + bo3_ensemble\n",
    "            bo3_mean = model_predictions[model]/len(score) + bo3_mean\n",
    "    \n",
    "    mase_all.append(np.vstack([\n",
    "                               mase_m4(train, test, models_des_predictions[0], m=m),\n",
    "                               mase_m4(train, test, ensemble_pred, m=m),\n",
    "                               mase_m4(train, test, mean_pred, m=m),\n",
    "                               mase_m4(train, test, groe_ensemble, m=m),\n",
    "                               mase_m4(train, test, bo3_ensemble, m=m),\n",
    "                               mase_m4(train, test, bo3_mean, m=m),\n",
    "                              ]))\n",
    "    smape_all.append(np.vstack([\n",
    "                                smape_m4(test, models_des_predictions[0]),\n",
    "                                smape_m4(test, ensemble_pred),\n",
    "                                smape_m4(test, mean_pred),\n",
    "                                smape_m4(test, groe_ensemble),\n",
    "                                smape_m4(test, bo3_ensemble),\n",
    "                                smape_m4(test, bo3_mean),\n",
    "                               ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MASE; Naive2: 1.098, Linear Regression: 1.068, Mean ensembling: 0.966, GROE ensembling: 0.973, BO3 ensembling: 1.003, BO3 Mean: 0.994\n",
      "sMAPE; Naive2: 8.974, Linear Regression: 8.678, Mean ensembling: 7.705, GROE ensembling: 7.830, BO3 ensembling: 8.160, BO3 Mean: 8.026\n"
     ]
    }
   ],
   "source": [
    "print(\"MASE; Naive2: {:.3f}, Linear Regression: {:.3f}, Mean ensembling: {:.3f}, GROE ensembling: {:.3f}, \"\n",
    "      \"BO3 ensembling: {:.3f}, BO3 Mean: {:.3f}\".format(*tuple(np.nanmean(np.stack(mase_all), axis=(0, 2)))))\n",
    "print(\"sMAPE; Naive2: {:.3f}, Linear Regression: {:.3f}, Mean ensembling: {:.3f}, GROE ensembling: {:.3f}, \"\n",
    "      \"BO3 ensembling: {:.3f}, BO3 Mean: {:.3f}\".format(*tuple(np.nanmean(np.stack(smape_all), axis=(0, 2)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OWA:  [4.25921273 4.11861286 3.65788532 3.71683597 3.87316115 3.80968261]\n"
     ]
    }
   ],
   "source": [
    "print(\"OWA: \", owa_m4(freq,\n",
    "                      np.nanmean(np.stack(mase_all), axis=(0, 2)),\n",
    "                      np.nanmean(np.stack(smape_all), axis=(0, 2))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression (Lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation MASE = 0.432')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['naiveS', 'theta', '4theta', 'naive2','SES', 'holt', 'damped']\n",
    "target_val.plot(label=\"target\", lw=3)\n",
    "pred = regr_model.predict(bktest_pred)\n",
    "pred.plot(label=\"ensemble\", lw=3)\n",
    "for i, mod in enumerate(bktest_pred):\n",
    "    mod.plot(label=labels[i])\n",
    "plt.legend()\n",
    "plt.title(\"Validation MASE = {:.3f}\".format(np.mean(mase_m4(train[:-len(test)], train[-len(test):], pred, m=m))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21215518, 0.63937082, 0.00330463, 0.14516937, 0.        ,\n",
       "       0.        , 0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_model.model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test MASE = 0.421')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['naiveS', 'theta', '4theta', 'naive2','SES', 'holt', 'damped']\n",
    "test.plot(label=\"target\", lw=3)\n",
    "ensemble_pred.plot(label=\"ensemble\", lw=3)\n",
    "for i,mod in enumerate(model_predictions):\n",
    "    mod.plot(label=labels[i])\n",
    "plt.legend()\n",
    "plt.title(\"Test MASE = {:.3f}\".format(np.mean(mase_m4(train, test, ensemble_pred, m=m))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GROE and other Ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MASE mean = 0.430, GROE = 0.496')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(label=\"target\", lw=3)\n",
    "mean_pred.plot(label='mean ensemble', lw=3)\n",
    "groe_ensemble.plot(label=\"groe ensemble\", lw=3)\n",
    "bo3_mean.plot(label=\"bo3 ensemble\")\n",
    "plt.legend()\n",
    "plt.title(\"MASE mean = {:.3f}, GROE = {:.3f}\".format(np.mean(mase_m4(train, test, mean_pred, m=m)), \n",
    "                                                     np.mean(mase_m4(train, test, groe_ensemble, m=m))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00705447, 0.14044403, 0.05853393, 0.43958416, 0.05530406,\n",
       "       0.18642169, 0.11265765])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GROE weights\n",
    "pesos"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}